Novel Concept of Modelling Embryology for Structuring an Artificial Neural Network

Ronald Thenius, Thomas Schmickl, Karl Crailsheim
Proceedings MATHMOD 09 Vienna 35 (2009), 8


The organisation of an Artificial Neural Network (e.g., the organisation in layers, the
number of cells per layer, the degree of connectivity between the cells) has a big influence on its
abilities (e.g., learning ability). In our work we present a novel method to organise the nodes and links
of an Artificial Neural Network in a biologically motivated manner using virtual embryology. For this
purpose we developed a virtual embryogenesis, which mimics processes observable in biology. In our
system a virtual embryo consists of individual cells, controlled by a genome. These cells can develop
to nodes in the ANN during the embryogenetic process. The embryo is implemented as a spatially
discrete and temporally discrete multi-agent model. The cells in our model interact with each other via
virtual physics and via virtual chemistry. With the work at hand, we show that patterns developing in
our virtual embryogenesis are comparable to patterns found during natural embryogenesis. We plan
to combine the described virtual embryology with Evolutionary Algorithms to optimise the genome of
the embryo. We expect the described model of virtual embryology (in combination with Evolutionary
Algorithms) to lead to novel, evolutionary shaped net structures of Artificial Neural Networks.

Download PDF: